Benchmark evaluating implicit neural representations for zebrafish brain atlas registration and microscopy reconstruction tasks.
Data-driven superresolution technique using conditional normalizing flows to recover fine-grained calorimeter information in high energy physics simulations.
Study of why minimal GPTs fail at out-of-distribution arithmetic generalization, revealing staged failures from layout barriers to positional encoding limits.
Survey of uncertainty-aware explainable AI methods, examining integration of uncertainty quantification (Bayesian, Monte Carlo, Conformal) into explanatory pipelines.
Controlled evaluation of LLM implementation choices for political text annotation, testing model selection, size, and prompt engineering best practices.
Comparative evaluation of physics-informed neural networks and neural ODEs for modeling nonlinear neuronal dynamics on Morris-Lecar model.
KOMET: model-agnostic framework using Koopman operators to track parameter evolution and handle temporal domain drift in non-stationary environments.
Graph-supported barycenter computation for probability measures using Riemannian geometry for signal processing and machine learning applications.
Multimodal deep learning combining RGB and thermal imaging on Raspberry Pi for diabetic foot ulcer staging classification and monitoring.
ASTER: agentic toolkit using LLMs for exoplanet research workflows, combining archival queries, literature search, and radiative transfer models.
Graph attention network classifier for autism spectrum disorder detection using fMRI brain imaging data and graph convolutional networks.
Online statistical inference framework for sample-averaged Q-learning to reduce variance and improve stability in reinforcement learning.
Analysis of reliability limits in LLM-based multi-agent planning systems modeled as decision networks with language-based communication constraints.
FormalProofBench: benchmark evaluating whether LLMs can produce formally verified graduate-level mathematical proofs using Lean 4.
Status update systems for wireless channels with energy constraints, modeling information freshness as expiring coupons for control applications.
Speaker anonymization system that neutralizes non-native accents to sound native-like while preserving timbre for real-time streaming applications.
Lightweight neural network for super-resolution imaging from low-resolution SPAD arrays, reconstructing 256x256 images on embedded devices.
Parameter estimation for stochastic differential equations using Wiener chaos expansion and stochastic gradient descent for computational efficiency.
Comparative study evaluating YOLO object detection models on robotics tasks using custom and COCO2017 datasets for workspace object detection.
Nuclear material identification using X-ray radiography, gamma-ray spectroscopy, and neutron measurements for plutonium sphere detection.
Study of incentive collapse paradox in AI-assisted task delegation showing accuracy improvements require unbounded payments without intervention mechanisms.
Persona-based LLM approach for simulating diverse human opinions at population scale for social science interventions and consequence modeling.
Theoretical analysis of loss landscape geometry in regularized deep matrix factorization proving unique minimizers under weight decay.
Information-theoretic framework for forecasting measuring mutual information between future observations and information set as predictability limit.
Sovereign Context Protocol defines open runtime attribution layer for human-generated content used in LLM training and inference.
Systematic evaluation of segmentation and geospatial foundation models for global field boundary segmentation using FTW benchmark.
Bayes-MICE extends multiple imputation for time series missing data using Bayesian inference and MCMC sampling.
Landmark-guided pose scoring system for automated transducer positioning in point-of-care cardiac ultrasound acquisition.
Pan-cancer immune landscape mapping through metagene clustering and predictive modeling to identify immunotherapy response drivers.
Weakly convex ridge regularizer for 3D non-Cartesian MRI reconstruction providing stable variational alternative to deep learning methods.
Conformal Prediction Assessment framework for evaluating conditional coverage validity in distribution-free prediction with finite-sample guarantees.
LightMover framework for controllable light manipulation in images using video diffusion priors for physically plausible illumination editing.
Unsupervised evaluation of nine open-source pre-trained audio models on music structure analysis without requiring annotated training data.
Study evaluating seven pre-trained deep learning models for predicting groove ratings from audio signals versus handcrafted features.
StretchCast global-regional AI weather forecasting framework using variable-resolution cubed-sphere mesh for refined regional predictions.
Comparative analysis of AI datasets, foundation models, and barriers to achieving general-purpose AI in surgical image analysis.
D-SPEAR dual-stream replay mechanism for stable off-policy reinforcement learning in robotic manipulation with contact-rich dynamics.
Rainbow-DemoRL combines multiple demonstration-augmented reinforcement learning strategies to improve sample efficiency using offline data.
Theoretical study of relationships between Bayesian networks and structural causal models for probabilistic graphical modeling.
CarbonEdge framework for carbon-aware deep learning inference at network edge, extending model partitioning to optimize environmental impact alongside latency.
High-performance engine for low-bit matrix-vector multiplication enabling efficient inference in neural networks, vector databases, and LLMs.
Open-source benchmark evaluating four AI-powered people search platforms across 119 queries for recruiting, sales, and expert search use cases.
Introduces Hidden Ads backdoor attack class exploiting Vision-Language Models' recommendation behavior to inject unauthorized advertisements through natural triggers.
Extends Bellman Deviation Detection framework for model-free RL to detect man-in-the-middle attacks in cyber-physical systems with refined MDP attack models.
RTLSeek uses multi-stage reinforcement learning to improve LLM-based RTL/Verilog generation with diverse hardware design implementations.
Composer paradigm for test-time instance-specific parameter composition enabling adaptive generative models.
Neural Gaussian mixture model using energy score guidance for predictive uncertainty quantification in machine learning.
LVRPO framework for language-visual alignment in multimodal foundation models using GRPO for understanding and generation.
KAT-Coder-V2 agentic coding model using five expert domains with specialized fine-tuning and unified distillation for software engineering tasks.
Empirical likelihood framework for nonsmooth functionals in policy evaluation and statistical inference.